Results 151 to 160 of about 7,912 (188)

Loglinear Marginal Models

2009
Loglinear models provide the most flexible tools for analyzing relationships among categorical variables in complex tables. It will be shown in this chapter how to apply these models in the context of marginal modeling. First, in Section 2.1, the basics of ordinary loglinear modeling will be explained.
Wicher Bergsma, Marcel Croon, Jacques A. Hagenaars   +2 more
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Hierarchische loglineare Modelle

2017
Im Kapitel 2.2.2. haben wir uns mit der Darstellung von Datensatzen befasst, die qualitative Merkmale enthalten. Wir haben gelernt, die Haufigkeitsverteilung von mehreren qualitativen Merkmalen in einer Kontingenztabelle zusammenzustellen. Wir wollen uns nun mit Modellen beschaftigen, die die Abhangigkeitsstruktur zwischen den Merkmalen beschreiben ...
Torben Kuhlenkasper, Andreas Handl
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Optimal prediction in loglinear models

Journal of Econometrics, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Logit Loglinear and Hierarchical Loglinear Modeling for Outcome Categories (445 Patients)

2020
Multinomial regression is adequate for identifying the main predictors of certain outcome categories, like different levels of injury or quality of life (QOL) (see also Chap. 28). An alternative approach is logit loglinear modeling. The latter method does not use continuous predictors on a case by case basis, but rather the weighted means of these ...
Ton J. Cleophas, Aeilko H. Zwinderman
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Visualizing parameters from loglinear models

Computational Statistics, 2004
An interactive graphical display for the parameters of loglinear models for categorical data is described. It is demonstrated how this display can be used for the analysis of the dependence structure of data, especially, for the identification of non-hierarchical models. The Berkeley admission and Knowledge of cancer data sets are used in the examples.
Valero-Mora, Pedro, Rodrigo, María F., Young, Forrest W.   +2 more
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Negative binomial loglinear mixed models

Statistical Modelling, 2003
The Poisson loglinear model is a common choice for explaining variability in counts. However, in many practical circumstances the restriction that the mean and variance are equal is not realistic. Overdispersion with respect to the Poisson distribution can be modeled explicitly by integrating with respect to a mixture distribution, and use of the ...
Booth, James G., Casella, George, Friedl, Herwig, Hobert, James P.   +3 more
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LOGLINEAR MODELING WITH INEXPENSIVE COMPUTING EQUIPMENT

American Journal of Epidemiology, 1984
Loglinear models are finding increasing application in the analysis of data from epidemiologic studies and increasing attention in statistics courses taken by epidemiologists in training. This paper describes a program for microcomputers, written in BASIC, which fits hierarchic loglinear models to categoric data organized into multiway contingency ...
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Loglinear models

2022
Pat Dugard, John Todman, Harry Staines
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Using standardised tables for interpreting Loglinear models

Quality & Quantity, 2005
Loglinear models are a useful but under-utilised research tool. One of the reasons for this is the difficulty of explaining model coefficients to others. Presenting coefficients in the form of standardised tables can help communicate the results to readers with little or no background in loglinear models.
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Loglinear Multivariate and Mixture Rasch Models

2007
In this chapter, Rasch models (RMs) are derived from a stochastic subject model. Fixed-effects RMs are shown to be equivalent to loglinear models with raw-score variables; random-effects RMs are equivalent to loglinear models with latent class variables. Within the larger framework of loglinear models, various extensions of the RM can be formulated. We
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